Biometric recognition has attracted much attention in the past decades.
Commonly used examples of biometric recognition include methods of recognizing one's face, iris, voice, ear, palm print, gait, or signature [1]}.
Face recognition is one of the most popular forms of biometric recognition and its development has made great progress in the last decades, mainly influenced by the availability of different open-source methods for face processing, including face and facial landmark detection and face recognition [2]}.
Furthermore, its field of application is very versatile, as almost every mobile device, including laptops and smartphones, nowadays offers the possibility to unlock its screen through face recognition.
Another popular application is video surveillance and through security cameras [3]} where face recognition can help to identify criminals or find missing persons.
In these and many other fields, the need for robust facial recognition systems has increased year over year [4]} and already in 2007 automatic face recognition has superseded human performance in controlled and constrained environments [5]}.
Thus, in security-relevant applications such as automatic border control, frontal faces, neutral expressions, and good illumination are enforced [6]}.
However, such an environment can not always be found.
Especially in outdoor surveillance situations, illumination from the sun is often not ideal for recognizing faces and people may show different expressions and will likely not look into the camera [7]}.
Furthermore, subjects may wear hats or glasses, faces might be partially occluded, and the quality and size of the image can vary greatly [8]}.
All these conditions can seriously interfere with the performance of face recognition.
<FIGURE>